We Asked the Experts: The WHO Surgical Safety Checklist and the COVID‐19 Pandemic: Recommendations for Content and Implementation Adaptations
Bibliographic record
Abstract
BACKGROUND: As surgical systems are forced to adapt and respond to new challenges, so should the patient safety tools within those systems. We sought to determine how the WHO SSC might best be adapted during the COVID-19 pandemic. METHODS: 18 Panelists from five continents and multiple clinical specialties participated in a three-round modified Delphi technique to identify potential recommendations, assess agreement with proposed recommendations and address items not meeting consensus. RESULTS: From an initial 29 recommendations identified in the first round, 12 were identified for inclusion in the second round. After discussion of recommendations without consensus for inclusion or exclusion, four additional recommendations were added for an eventual 16 recommendations. Nine of these recommendations were related to checklist content, while seven recommendations were related to implementation. CONCLUSIONS: This multinational panel has identified 16 recommendations for sites looking to use the surgical safety checklist during the COVID-19 pandemic. These recommendations provide an example of how the SSC can adapt to meet urgent and emerging needs of surgical systems by targeting important processes and encouraging critical discussions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".